{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "02134a5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#| hide\n",
    "!pip install -Uqq nixtla"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c6d8f223",
   "metadata": {},
   "outputs": [],
   "source": [
    "#| hide \n",
    "from nixtla.utils import in_colab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3c6c0333",
   "metadata": {},
   "outputs": [],
   "source": [
    "#| hide \n",
    "IN_COLAB = in_colab()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ce98fab5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#| hide\n",
    "if not IN_COLAB:\n",
    "    from nixtla.utils import colab_badge\n",
    "    from dotenv import load_dotenv"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da753996-54f8-4244-a34e-7316b0c01827",
   "metadata": {},
   "source": [
    "# Fine-tuning"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75a62889-d81e-462e-b235-c1eba1096da9",
   "metadata": {},
   "source": [
    "Fine-tuning is a powerful process for utilizing TimeGPT more effectively. Foundation models such as TimeGPT are pre-trained on vast amounts of data, capturing wide-ranging features and patterns. These models can then be specialized for specific contexts or domains. With fine-tuning, the model's parameters are refined to forecast a new task, allowing it to tailor its vast pre-existing knowledge towards the requirements of the new data. Fine-tuning thus serves as a crucial bridge, linking TimeGPT's broad capabilities to your tasks specificities.\n",
    "\n",
    "Concretely, the process of fine-tuning consists of performing a certain number of training iterations on your input data minimizing the forecasting error. The forecasts will then be produced with the updated model. To control the number of iterations, use the `finetune_steps` argument of the `forecast` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "448eaf77-0a40-4b5b-88a2-31de99f404bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "[![](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Nixtla/nixtla/blob/main/nbs/docs/tutorials/06_finetuning.ipynb)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#| echo: false\n",
    "if not IN_COLAB:\n",
    "    load_dotenv()    \n",
    "    colab_badge('docs/tutorials/06_finetuning')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10ec4f03",
   "metadata": {},
   "source": [
    "## 1. Import packages\n",
    "First, we import the required packages and initialize the Nixtla client"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "98942108-d427-42d6-81f8-fa0bb5859395",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from nixtla import NixtlaClient"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "64178d1c-957e-4a04-ab64-fde332b1840c",
   "metadata": {},
   "outputs": [],
   "source": [
    "nixtla_client = NixtlaClient(\n",
    "    # defaults to os.environ.get(\"NIXTLA_API_KEY\")\n",
    "    api_key = 'my_api_key_provided_by_nixtla'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b57a38e6",
   "metadata": {},
   "source": [
    "> 👍 Use an Azure AI endpoint\n",
    "> \n",
    "> To use an Azure AI endpoint, remember to set also the `base_url` argument:\n",
    "> \n",
    "> `nixtla_client = NixtlaClient(base_url=\"you azure ai endpoint\", api_key=\"your api_key\")`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5cd61549-0b00-4a42-a98e-239fa4fae5e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#| hide\n",
    "if not IN_COLAB:\n",
    "    nixtla_client = NixtlaClient()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c2e5387",
   "metadata": {},
   "source": [
    "## 2. Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b78cc83e-7d34-4c37-906d-8c7ed1a977fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timestamp</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1949-01-01</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1949-02-01</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1949-03-01</td>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1949-04-01</td>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1949-05-01</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    timestamp  value\n",
       "0  1949-01-01    112\n",
       "1  1949-02-01    118\n",
       "2  1949-03-01    132\n",
       "3  1949-04-01    129\n",
       "4  1949-05-01    121"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('https://raw.githubusercontent.com/Nixtla/transfer-learning-time-series/main/datasets/air_passengers.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09be4766",
   "metadata": {},
   "source": [
    "## 3. Fine-tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a683abc7-190c-40a6-a4e8-41a4c64bd773",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:nixtlats.nixtla_client:Validating inputs...\n",
      "INFO:nixtlats.nixtla_client:Preprocessing dataframes...\n",
      "INFO:nixtlats.nixtla_client:Inferred freq: MS\n",
      "INFO:nixtlats.nixtla_client:Calling Forecast Endpoint...\n"
     ]
    }
   ],
   "source": [
    "timegpt_fcst_finetune_df = nixtla_client.forecast(\n",
    "    df=df, h=12, finetune_steps=10,\n",
    "    time_col='timestamp', target_col='value',\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac469746",
   "metadata": {},
   "source": [
    "> 📘 Available models in Azure AI\n",
    ">\n",
    "> If you are using an Azure AI endpoint, please be sure to set `model=\"azureai\"`:\n",
    ">\n",
    "> `nixtla_client.forecast(..., model=\"azureai\")`\n",
    "> \n",
    "> For the public API, we support two models: `timegpt-1` and `timegpt-1-long-horizon`. \n",
    "> \n",
    "> By default, `timegpt-1` is used. Please see [this tutorial](https://docs.nixtla.io/docs/tutorials-long_horizon_forecasting) on how and when to use `timegpt-1-long-horizon`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "545ffdac-f166-417b-993f-78f51b0db6a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2400x350 with 1 Axes>"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nixtla_client.plot(\n",
    "    df, timegpt_fcst_finetune_df, \n",
    "    time_col='timestamp', target_col='value',\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62fc9cba-7c6e-4aef-9c68-e05d4fe8f7ba",
   "metadata": {},
   "source": [
    "In this code, `finetune_steps=10` means the model will go through 10 iterations of training on your time series data.\n",
    "\n",
    "Keep in mind that fine-tuning can be a bit of trial and error. You might need to adjust the number of `finetune_steps` based on your specific needs and the complexity of your data. It's recommended to monitor the model's performance during fine-tuning and adjust as needed. Be aware that more `finetune_steps` may lead to longer training times and could potentially lead to overfitting if not managed properly. \n",
    "\n",
    "Remember, fine-tuning is a powerful feature, but it should be used thoughtfully and carefully."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c546351",
   "metadata": {},
   "source": [
    "For a detailed guide on using a specific loss function for fine-tuning, check out the [Fine-tuning with a specific loss function](https://docs.nixtla.io/docs/tutorials-fine_tuning_with_a_specific_loss_function) tutorial."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "python3",
   "language": "python",
   "name": "python3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
